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Relations (1)
related 2.00 — strongly supporting 3 facts
Large Language Models utilize prompt engineering techniques like Graph of Thoughts (GoT) to enhance their reasoning capabilities as noted in [1] and [2], although they still face specific challenges in merging divergent results within the GoT framework as described in [3].
Facts (3)
Sources
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org 2 facts
claimPrompt engineering techniques, including Chain of Thought (CoT), Tree of Thought (ToT), Graph of Thoughts (GoT), and ReAct (Reason and Act), have demonstrated significant improvements in the reasoning abilities and task-specific actions of Large Language Models.
claimPrompt engineering techniques, including Chain of Thought (CoT), Tree of Thought (ToT), Graph of Thoughts (GoT), and ReAct (Reason and Act), have demonstrated significant improvements in the reasoning abilities and task-specific actions of Large Language Models.
Grounding LLM Reasoning with Knowledge Graphs - arXiv arxiv.org 1 fact
claimLarge Language Models (LLMs) struggle to effectively merge divergent results from multiple branches in the Graph of Thought (GoT) reasoning strategy, specifically failing to combine different triples found in separate branches during graph exploration.